Autonomous driving based on deep neural network

Deep learning, the critical part of machine learning, has become influential in different fields, including natural language processing, computational biology, and computer vision. In the last decade, there has been a massive surge in computer vision research since its related application is so promising. Many have proposed various methods to fulfill the automation of driving based on deep learning, but, up until now, there is still a gap between the virtual and reality. This paper focuses on its application in autonomous driving. A new framework is proposed to fill that gap using a deep neural network. Specifically, instead of using the raw images captured by cameras to make decisions, semantic segmentation is applied first to get intermediate products that can better connect virtual and reality. Considering the road landscape needs to be mainly treated, the pre-trained model PSPNet is used to process the original image data. Then this data is provided as input to a deep CNN model for feature extraction and prediction. Compared to the traditional method, a semantic segmentation process is added to help extract useful information within an image and is expected to bring some positive effects.